Merge branch 'main' into update_ar1_turningpts

mmcky · mmcky · commit d4612de2affe · 2025-09-01T08:19:42.000+10:00
diff --git a/.github/workflows/cache.yml b/.github/workflows/cache.yml
@@ -17,7 +17,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
@@ -15,7 +15,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
@@ -16,7 +16,7 @@ jobs:
           auto-update-conda: true
           auto-activate-base: true
           miniconda-version: 'latest'
-          python-version: "3.12"
+          python-version: "3.13"
           environment-file: environment.yml
           activate-environment: quantecon
       - name: Install JAX, Numpyro, PyTorch
diff --git a/environment.yml b/environment.yml
@@ -2,17 +2,18 @@ name: quantecon
 channels:
   - default
 dependencies:
-  - python=3.12
-  - anaconda=2024.10
+  - python=3.13
+  - anaconda=2025.06
   - pip
   - pip:
-    - jupyter-book==1.0.3
-    - quantecon-book-theme==0.7.6
-    - sphinx-tojupyter==0.3.0
+    - jupyter-book==1.0.4post1
+    - quantecon-book-theme==0.9.0
+    - sphinx-tojupyter==0.3.1
     - sphinxext-rediraffe==0.2.7
-    - sphinx-reredirects==0.1.4
     - sphinx-exercise==1.0.1
-    - sphinx-proof==0.2.0
-    - ghp-import==1.1.0
-    - sphinxcontrib-youtube==1.3.0 #Version 1.3.0 is required as quantecon-book-theme is only compatible with sphinx<=5
+    - sphinx-proof==0.2.1
+    - sphinxcontrib-youtube==1.4.1
     - sphinx-togglebutton==0.3.2
+    - sphinx-reredirects==0.1.4
+
+
diff --git a/lectures/_config.yml b/lectures/_config.yml
@@ -98,6 +98,9 @@ sphinx:
       advanced:
         - "https://python-advanced.quantecon.org"
         - null
+      jax:
+        - "https://jax.quantecon.org/"
+        - null
     mathjax3_config:
       tex:
         macros:
diff --git a/lectures/aiyagari.md b/lectures/aiyagari.md
@@ -57,7 +57,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon.markov import DiscreteDP
 from numba import jit
diff --git a/lectures/cake_eating_problem.md b/lectures/cake_eating_problem.md
@@ -41,7 +41,6 @@ In what follows, we require the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 
diff --git a/lectures/career.md b/lectures/career.md
@@ -48,7 +48,6 @@ We begin with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numba import jit, prange
@@ -370,7 +369,8 @@ when the worker follows the optimal policy.
 
 In particular, modulo randomness, reproduce the following figure (where the horizontal axis represents time)
 
-```{figure} /_static/lecture_specific/career/career_solutions_ex1_py.png
+```{image} /_static/lecture_specific/career/career_solutions_ex1_py.png
+:align: center
 ```
 
 ```{hint}
diff --git a/lectures/cass_koopmans_2.md b/lectures/cass_koopmans_2.md
@@ -67,7 +67,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import jit, float64
 from numba.experimental import jitclass
 import numpy as np
diff --git a/lectures/exchangeable.md b/lectures/exchangeable.md
@@ -71,7 +71,6 @@ Let’s start with some imports:
 tags: [hide-output]
 ---
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import jit, vectorize
 from math import gamma
 import scipy.optimize as op
diff --git a/lectures/finite_markov.md b/lectures/finite_markov.md
@@ -53,7 +53,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import quantecon as qe
 import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
@@ -1113,7 +1112,8 @@ is known as [PageRank](https://en.wikipedia.org/wiki/PageRank).
 
 To illustrate the idea, consider the following diagram
 
-```{figure} /_static/lecture_specific/finite_markov/web_graph.png
+```{image} /_static/lecture_specific/finite_markov/web_graph.png
+:align: center
 ```
 
 Imagine that this is a miniature version of the WWW, with
diff --git a/lectures/ifp.md b/lectures/ifp.md
@@ -548,7 +548,8 @@ Let's consider how the interest rate affects consumption.
 
 Reproduce the following figure, which shows (approximately) optimal consumption policies for different interest rates
 
-```{figure} /_static/lecture_specific/ifp/ifp_policies.png
+```{image} /_static/lecture_specific/ifp/ifp_policies.png
+:align: center
 ```
 
 * Other than `r`, all parameters are at their default values.
diff --git a/lectures/inventory_dynamics.md b/lectures/inventory_dynamics.md
@@ -48,7 +48,6 @@ Let's start with some imports
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit, float64, prange
 from numba.experimental import jitclass
diff --git a/lectures/kalman.md b/lectures/kalman.md
@@ -565,7 +565,8 @@ In the simulation, take $\theta = 10$, $\hat x_0 = 8$ and $\Sigma_0 = 1$.
 
 Your figure should -- modulo randomness -- look something like this
 
-```{figure} /_static/lecture_specific/kalman/kl_ex1_fig.png
+```{image} /_static/lecture_specific/kalman/kl_ex1_fig.png
+:align: center
 ```
 
 ```{exercise-end}
@@ -629,7 +630,8 @@ Plot $z_t$ against $T$, setting $\epsilon = 0.1$ and $T = 600$.
 
 Your figure should show error erratically declining something like this
 
-```{figure} /_static/lecture_specific/kalman/kl_ex2_fig.png
+```{image} /_static/lecture_specific/kalman/kl_ex2_fig.png
+:align: center
 ```
 
 ```{exercise-end}
@@ -732,7 +734,8 @@ Finally, set $x_0 = (0, 0)$.
 
 You should end up with a figure similar to the following (modulo randomness)
 
-```{figure} /_static/lecture_specific/kalman/kalman_ex3.png
+```{image} /_static/lecture_specific/kalman/kalman_ex3.png
+:align: center
 ```
 
 Observe how, after an initial learning period, the Kalman filter performs quite well, even relative to the competitor who predicts optimally with knowledge of the latent state.
diff --git a/lectures/lake_model.md b/lectures/lake_model.md
@@ -69,7 +69,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon import MarkovChain
 from scipy.stats import norm
diff --git a/lectures/likelihood_bayes.md b/lectures/likelihood_bayes.md
@@ -52,7 +52,6 @@ We'll begin by loading some Python modules.
 :hide-output: false
 
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import vectorize, jit, prange
 from math import gamma
diff --git a/lectures/linear_algebra.md b/lectures/linear_algebra.md
@@ -74,7 +74,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
diff --git a/lectures/lln_clt.md b/lectures/lln_clt.md
@@ -52,7 +52,6 @@ We'll need the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import random
 import numpy as np
 from scipy.stats import t, beta, lognorm, expon, gamma, uniform
diff --git a/lectures/lq_inventories.md b/lectures/lq_inventories.md
@@ -204,7 +204,6 @@ its consequences.
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 ```
diff --git a/lectures/lqcontrol.md b/lectures/lqcontrol.md
@@ -71,7 +71,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from quantecon import LQ
 ```
diff --git a/lectures/markov_asset.md b/lectures/markov_asset.md
@@ -73,7 +73,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
 from numpy.linalg import eigvals, solve
diff --git a/lectures/markov_perf.md b/lectures/markov_perf.md
@@ -722,7 +722,8 @@ c1 = c2 = np.array([1, -2, 1])
 e1 = e2 = np.array([10, 10, 3])
 ```
 
-```{figure} /_static/lecture_specific/markov_perf/judd_fig2.png
+```{image} /_static/lecture_specific/markov_perf/judd_fig2.png
+:align: center
 ```
 
 Inventories trend to a common steady state.
@@ -731,7 +732,8 @@ If we increase the depreciation rate to $\delta = 0.05$, then we expect steady s
 
 This is indeed the case, as the next figure shows
 
-```{figure} /_static/lecture_specific/markov_perf/judd_fig1.png
+```{image} /_static/lecture_specific/markov_perf/judd_fig1.png
+:align: center
 ```
 
 In this exercise, reproduce the figure when $\delta = 0.02$.
diff --git a/lectures/mccall_model.md b/lectures/mccall_model.md
@@ -61,7 +61,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit, float64
 from numba.experimental import jitclass
diff --git a/lectures/mix_model.md b/lectures/mix_model.md
@@ -124,7 +124,6 @@ As usual, we'll start by importing some Python tools.
 :hide-output: false
 
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import vectorize, jit
 from math import gamma
diff --git a/lectures/mle.md b/lectures/mle.md
@@ -44,7 +44,6 @@ We'll require the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numpy import exp
 from scipy.special import factorial, gammaln
diff --git a/lectures/multi_hyper.md b/lectures/multi_hyper.md
@@ -112,7 +112,6 @@ Let's start with some imports.
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from scipy.special import comb
 from scipy.stats import normaltest
diff --git a/lectures/multivariate_normal.md b/lectures/multivariate_normal.md
@@ -62,7 +62,6 @@ We  use the following imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import jit
 import statsmodels.api as sm
diff --git a/lectures/newton_method.md b/lectures/newton_method.md
@@ -29,8 +29,7 @@ kernelspec:
 ```
 
 ```{seealso}
-**GPU:** A version of this lecture which makes use of [jax](https://jax.readthedocs.io) to run the code
-on a `GPU` is [available here](https://jax.quantecon.org/newtons_method.html)
+A version of this lecture using [JAX](https://github.com/jax-ml/jax) is {doc}`available here <jax:newtons_method>`
 ```
 
 ## Overview
@@ -90,8 +89,6 @@ from scipy.optimize import root
 from autograd import jacobian
 # Thinly-wrapped numpy to enable automatic differentiation
 import autograd.numpy as np
-
-plt.rcParams["figure.figsize"] = (10, 5.7)
 ```
 
 ## Fixed Point Computation Using Newton's Method
@@ -790,7 +787,7 @@ With the larger overhead, the speed is not better than the optimized `scipy` fun
 Our next step is to investigate a large market with 3,000 goods.
 
 A JAX version of this section using GPU accelerated linear algebra and
-automatic differentiation is available [here](https://jax.quantecon.org/newtons_method.html#application)
+automatic differentiation is {doc}`available here <jax:newtons_method>`
 
 The excess demand function is essentially the same, but now the matrix $A$ is $3000 \times 3000$ and the parameter vectors $b$ and $c$ are $3000 \times 1$.
 
diff --git a/lectures/ols.md b/lectures/ols.md
@@ -59,7 +59,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import pandas as pd
 import statsmodels.api as sm
@@ -604,7 +603,7 @@ results.
 
 ```{code-cell} python3
 # Load in data
-df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python/blob/master/source/_static/lecture_specific/ols/maketable4.dta?raw=true')
+df4 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable4.dta')
 
 # Add a constant term
 df4['const'] = 1
@@ -677,7 +676,7 @@ using `numpy` - your results should be the same as those in the
 
 ```{code-cell} python3
 # Load in data
-df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python/blob/master/source/_static/lecture_specific/ols/maketable1.dta?raw=true')
+df1 = pd.read_stata('https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/ols/maketable1.dta')
 df1 = df1.dropna(subset=['logpgp95', 'avexpr'])
 
 # Add a constant term
diff --git a/lectures/optgrowth.md b/lectures/optgrowth.md
@@ -61,7 +61,6 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from scipy.interpolate import interp1d
 from scipy.optimize import minimize_scalar
diff --git a/lectures/optgrowth_fast.md b/lectures/optgrowth_fast.md
@@ -344,7 +344,8 @@ $$
 The next figure shows a simulation of 100 elements of this sequence for three
 different discount factors (and hence three different policies).
 
-```{figure} /_static/lecture_specific/optgrowth/solution_og_ex2.png
+```{image} /_static/lecture_specific/optgrowth/solution_og_ex2.png
+:align: center
 ```
 
 In each sequence, the initial condition is $y_0 = 0.1$.
diff --git a/lectures/pandas_panel.md b/lectures/pandas_panel.md
@@ -502,7 +502,7 @@ in Europe by age and sex from [Eurostat](https://ec.europa.eu/eurostat/data/data
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url3 = 'https://raw.githubusercontent.com/QuantEcon/lecture-python/master/source/_static/lecture_specific/pandas_panel/employ.csv'
+url3 = 'https://github.com/QuantEcon/lecture-python.myst/raw/refs/heads/main/lectures/_static/lecture_specific/pandas_panel/employ.csv'
 ```
 
 Reading in the CSV file returns a panel dataset in long format. Use `.pivot_table()` to construct
diff --git a/lectures/perm_income.md b/lectures/perm_income.md
@@ -48,7 +48,6 @@ Let's start with some imports
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import random
 from numba import jit
diff --git a/lectures/rational_expectations.md b/lectures/rational_expectations.md
@@ -68,7 +68,6 @@ Let's start with some standard imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 ```
 
diff --git a/lectures/samuelson.md b/lectures/samuelson.md
diff --git a/lectures/sir_model.md b/lectures/sir_model.md
diff --git a/lectures/uncertainty_traps.md b/lectures/uncertainty_traps.md
diff --git a/lectures/wealth_dynamics.md b/lectures/wealth_dynamics.md
